In the field of artificially intelligent computer systems capable of answering questions posed in natural language, cognitive question answering (QA) systems (such as the IBM Watson™ artificially intelligent computer system or and other natural language question answering systems) process questions posed in natural language to determine answers and associated confidence scores based on knowledge acquired by the QA system. To process and answer questions, the QA system may be trained with question-answer (QA) pairs derived from a database or corpus of knowledge, but the resulting answers can be incorrect or inaccurate for a variety of reasons relating to the peculiarities of language constructs and human reasoning for understanding same. For example, analogies are language constructs which enable people to transfer knowledge from one situation or context (the source) to another (the target) based on a conceptual similarity therebetween, and provide powerful cognitive mechanisms or tools that can be used to explain something that is unknown in terms of a related concept that is known to someone. At the core of analogical reasoning lies the concept of similarity, but the process of understanding an analogy requires reasoning from a relational perspective that can be challenging, especially for people learning a new language since the word-for-word translation may not capture the essence of the original statement. In addition, automated QA systems and other natural language systems which come across an analogy in a question or answer corpus will also have a difficult time with identifying and understanding analogies. As a result, the existing solutions for efficiently identifying and understanding analogies for training and/or use by a QA system are extremely difficult at a practical level.